 # Matrix Representation

## Presentation on theme: "Matrix Representation"— Presentation transcript:

Matrix Representation
Matrix Rep. Same basics as introduced already. Convenient method of working with vectors. Superposition Complete set of vectors can be used to express any other vector. Complete set of N vectors can form other complete sets of N vectors. Can find set of vectors for Hermitian operator satisfying Eigenvectors and eigenvalues Matrix method Find superposition of basis states that are eigenstates of particular operator. Get eigenvalues. Copyright – Michael D. Fayer, 2007

Orthonormal basis set in N dimensional vector space
basis vectors Any vector can be written as with To get this, project out Copyright – Michael D. Fayer, 2007

Substituting the series in terms of bases vectors.
Operator equation Substituting the series in terms of bases vectors. Left mult. by The N2 scalar products are completely determined by N values of j ; N for each yi Copyright – Michael D. Fayer, 2007

Matrix elements of A in the basis
Writing Matrix elements of A in the basis gives for the linear transformation Know the aij because we know A and In terms of the vector representatives (Set of numbers, gives you vector when basis is known.) vector vector representative, must know basis The set of N linear algebraic equations can be written as double underline means matrix Copyright – Michael D. Fayer, 2007

array of coefficients - matrix
The aij are the elements of the matrix The product of matrix and vector representative x is a new vector representative y with components Copyright – Michael D. Fayer, 2007

Matrix Properties, Definitions, and Rules
Two matrices, and are equal if aij = bij. The unit matrix ones down principal diagonal Gives identity transformation Corresponds to The zero matrix Copyright – Michael D. Fayer, 2007

Matrix multiplication
Consider operator equations Using the same basis for both transformations has elements Law of matrix multiplication Example Copyright – Michael D. Fayer, 2007

Multiplication Associative
Multiplication NOT Commutative Matrix addition and multiplication by complex number Inverse of a matrix inverse of identity matrix transpose of cofactor matrix (matrix of signed minors) determinant Copyright – Michael D. Fayer, 2007

interchange rows and columns Transpose
Reciprocal of Product For matrix defined as interchange rows and columns Transpose Complex Conjugate complex conjugate of each element Hermitian Conjugate complex conjugate transpose Copyright – Michael D. Fayer, 2007

transpose of product is product of transposes in reverse order
Rules transpose of product is product of transposes in reverse order determinant of transpose is determinant complex conjugate of product is product of complex conjugates determinant of complex conjugate is complex conjugate of determinant Hermitian conjugate of product is product of Hermitian conjugates in reverse order determinant of Hermitian conjugate is complex conjugate of determinant Copyright – Michael D. Fayer, 2007

Definitions Symmetric Hermitian Real Imaginary Unitary Diagonal
Powers of a matrix Copyright – Michael D. Fayer, 2007

Column vector representative one column matrix
then vector representatives in particular basis becomes row vector transpose of column vector transpose Hermitian conjugate Copyright – Michael D. Fayer, 2007

Change of Basis orthonormal basis then
Superposition of can form N new vectors linearly independent a new basis complex numbers Copyright – Michael D. Fayer, 2007

New Basis is Orthonormal
if the matrix coefficients in superposition meets the condition is unitary Important result. The new basis will be orthonormal if , the transformation matrix, is unitary (see book and Errata and Addenda ). Copyright – Michael D. Fayer, 2007

Same vector – different basis.
Unitary transformation substitutes orthonormal basis for orthonormal basis Vector Same vector – different basis. vector – line in space (may be high dimensionality abstract space) written in terms of two basis sets The unitary transformation can be used to change a vector representative of in one orthonormal basis set to its vector representative in another orthonormal basis set. x – vector rep. in unprimed basis x' – vector rep. in primed basis change from unprimed to primed basis change from primed to unprimed basis Copyright – Michael D. Fayer, 2007

Vector - line in real space.
Example Consider basis Vector line in real space. In terms of basis Vector representative in basis Copyright – Michael D. Fayer, 2007

Change basis by rotating axis system 45° around .
Can find the new representative of , s' is rotation matrix For 45° rotation around z Copyright – Michael D. Fayer, 2007

vector representative of in basis
Then vector representative of in basis Same vector but in new basis. Properties unchanged. Example – length of vector Copyright – Michael D. Fayer, 2007

Can go back and forth between representatives of a vector by
change from unprimed to primed basis change from primed to unprimed basis components of in different basis Copyright – Michael D. Fayer, 2007

Consider the linear transformation
operator equation In the basis can write or Change to new orthonormal basis using or with the matrix given by Because is unitary Copyright – Michael D. Fayer, 2007

Similarity Transformation
Extremely Important Can change the matrix representing an operator in one orthonormal basis into the equivalent matrix in a different orthonormal basis. Called Similarity Transformation Copyright – Michael D. Fayer, 2007

Relations unchanged by change of basis.
In basis Go into basis Relations unchanged by change of basis. Example Can insert between because Therefore Copyright – Michael D. Fayer, 2007

Isomorphism between operators in abstract vector space and matrix representatives.
Because of isomorphism not necessary to distinguish abstract vectors and operators from their matrix representatives. The matrices (for operators) and the representatives (for vectors) can be used in place of the real things. Copyright – Michael D. Fayer, 2007

Hermitian Operators and Matrices
Hermitian operator Hermitian Matrix + - complex conjugate transpose - Hermitian conjugate Copyright – Michael D. Fayer, 2007

Theorem (Proof: Powell and Craseman, P
Theorem (Proof: Powell and Craseman, P. 303 – 307, or linear algebra book) For a Hermitian operator A in a linear vector space of N dimensions, there exists an orthonormal basis, relative to which A is represented by a diagonal matrix The vectors, , and the corresponding real numbers, ai, are the solutions of the Eigenvalue Equation and there are no others. Copyright – Michael D. Fayer, 2007

Application of Theorem
Operator A represented by matrix in some basis The basis is any convenient basis. In general, the matrix will not be diagonal. There exists some new basis eigenvectors in which represents operator and is diagonal eigenvalues. To get from to unitary transformation. Similarity transformation takes matrix in arbitrary basis into diagonal matrix with eigenvalues on the diagonal. Copyright – Michael D. Fayer, 2007

Matrices and Q.M. Previously represented state of system by vector in abstract vector space. Dynamical variables represented by linear operators. Operators produce linear transformations. Real dynamical variables (observables) are represented by Hermitian operators. Observables are eigenvalues of Hermitian operators. Solution of eigenvalue problem gives eigenvalues and eigenvectors. Copyright – Michael D. Fayer, 2007

Matrix Representation
Hermitian operators replaced by Hermitian matrix representations. In proper basis, is the diagonalized Hermitian matrix and the diagonal matrix elements are the eigenvalues (observables). A suitable transformation takes (arbitrary basis) into (diagonal – eigenvector basis) Diagonalization of matrix gives eigenvalues and eigenvectors. Matrix formulation is another way of dealing with operators and solving eigenvalue problems. Copyright – Michael D. Fayer, 2007

All rules about kets, operators, etc. still apply.
Example Two Hermitian matrices can be simultaneously diagonalized by the same unitary transformation if and only if they commute. All ideas about matrices also true for infinite dimensional matrices. Copyright – Michael D. Fayer, 2007

Example – Harmonic Oscillator
Have already solved – use occupation number representation kets and bras (already diagonal). matrix elements of a Copyright – Michael D. Fayer, 2007

Copyright – Michael D. Fayer, 2007

Copyright – Michael D. Fayer, 2007

Adding the matrices and and multiplying by ½ gives
The matrix is diagonal with eigenvalues on diagonal. In normal units the matrix would be multiplied by This example shows idea, but not how to diagonalize matrix when you don’t already know the eigenvectors. Copyright – Michael D. Fayer, 2007

matrix representing operator representative of eigenvector
Diagonalization Eigenvalue equation eigenvalue matrix representing operator representative of eigenvector In terms of the components This represents a system of equations We know the aij. We don't know a - the eigenvalues ui - the vector representatives, one for each eigenvalue. Copyright – Michael D. Fayer, 2007

Besides the trivial solution
A solution only exists if the determinant of the coefficients of the ui vanishes. know aij, don't know a's Expanding the determinant gives Nth degree equation for the the unknown a's (eigenvalues). Then substituting one eigenvalue at a time into system of equations, the ui (eigenvector representatives) are found. N equations for u's gives only N - 1 conditions. Use normalization. Copyright – Michael D. Fayer, 2007

Example - Degenerate Two State Problem
Basis - time independent kets orthonormal. a and b not eigenkets. Coupling g. These equations define H. The matrix elements are And the Hamiltonian matrix is Copyright – Michael D. Fayer, 2007

The corresponding system of equations is
These only have a solution if the determinant of the coefficients vanish. Ground State E = 0 E0 2g Excited State Dimer Splitting Expanding Energy Eigenvalues Copyright – Michael D. Fayer, 2007

To obtain Eigenvectors Use system of equations for each eigenvalue.
Eigenvectors associated with l+ and l-. and are the vector representatives of and in the We want to find these. Copyright – Michael D. Fayer, 2007

First, for the eigenvalue write system of equations.
Matrix elements of The matrix elements are The result is Copyright – Michael D. Fayer, 2007

An equivalent way to get the equations is to use a matrix form.
Substitute Multiplying the matrix by the column vector representative gives equations. Copyright – Michael D. Fayer, 2007

The two equations are identical.
Always get N – 1 conditions for the N unknown components. Normalization condition gives necessary additional equation. Then and Eigenvector in terms of the basis set. Copyright – Michael D. Fayer, 2007

using the matrix form to write out the equations
For the eigenvalue using the matrix form to write out the equations Substituting These equations give Using normalization Therefore Copyright – Michael D. Fayer, 2007

Can diagonalize by transformation
diagonal not diagonal Transformation matrix consists of representatives of eigenvectors in original basis. complex conjugate transpose Copyright – Michael D. Fayer, 2007

after matrix multiplication
Then Factoring out after matrix multiplication more matrix multiplication diagonal with eigenvalues on diagonal Copyright – Michael D. Fayer, 2007